Immigrant Wages and Recessions: Evidence from Undocumented Mexicans

Transcription

1 Immigrant Wages and Recessions: Evidence from Undocumented Mexicans Rebecca Lessem and Kayuna Nakajima January 31, 2018 Abstract We study the impact of recessions on the real wages of undocumented immigrants in the US using data from the Mexican Migration Project. Empirical evidence shows that undocumented immigrants experience larger wage drops during recessions than natives, suggesting that the frequent renegotiation of contracts leads to greater wage flexibility. Because migration decisions also adjust to these wage changes, the observed equilibrium wages are capturing both lowered aggregate productivity and a smaller supply of migrant workers. To separate these effects, we analyze an equilibrium migration model where native wages are rigid while immigrant wages are flexible. In a counterfactual experiment with a fixed supply of immigrant workers, we see a stronger relationship between aggregate negative productivity shocks and immigrant wages. We also find that the flexibility of immigrant wages reduces the volatility of native employment over the business cycle. JEL codes: J61, J31 Lessem: Tepper School of Business, Carnegie Mellon University, rlessem@andrew.cmu.edu. Nakajima: Bates White LLC, kayuna.fukushima@bateswhite.com. We thank seminar participants at the Society of Labor Economists conference, the University of Wisconsin-Madison, the Federal Reserve of St. Louis, and the University of Calgary for helpful comments. Maria Cuellar and Erica VanSant provided excellent research assistance. 1

2 1. Introduction Data from recent years show that undocumented immigration rates dropped during the downturn in the US economy, most likely in response to weaker job opportunities. 1 However, little is known empirically on how undocumented immigrant wages change over the business cycle. Past work has found evidence of wage rigidity in the native population, implying that adjustments occur through the unemployment rate. This is typically explained by the permanence of labor market contracts. Due to the short-term nature of employment for undocumented immigrants, we might expect greater wage flexibility for this group than for natives. In this paper, we aim to understand how undocumented immigrant wages change over the business cycle. Using data from the Mexican Migration Project (MMP), we study how undocumented immigrant wages respond to labor market conditions in the US. These data provide a unique opportunity to study this issue since the survey reports both legal status and wages. When wages decrease, fewer people will choose to move to the US. 2 Because of this, the observed equilibrium wage is capturing both lowered aggregate productivity and a smaller supply of migrant workers. We build an equilibrium model, where a firm hires immigrant and native workers, to study how this mechanism affects immigrant wages. The flexibility of immigrant wages could also affect native outcomes. By looking at this in a unified setting, we can use the model to understand how the variations in migrant wages affect native employment fluctuations over the business cycle. Previous work has found greater flexibility in immigrant wages than in native wages, but did not differentiate between legal and undocumented immigrants. Bratsberg et al. (2006) and Orrenius and Zavodny (2010) find that immigrant wages are more cyclical than those of natives; however, they use CPS data, which does not report legal status. Similarly, Chiswick et al. (1997) find that the labor market status of immigrants is more sensitive to cyclical fluctuations than in the native-born population. By using the MMP, we are able to isolate the effect to the undocumented immigrant population. Furthermore, these papers only look at overall changes, and do not segregate the effects of lowered productivity and reduced migration rates. We use the MMP data to show that undocumented immigrant wages decrease as the US unemployment rate increases. In comparison to previous work, we control for selection by looking at the wage growth of immigrants over repeated trips to the US, and again find a negative correlation between wages and the unemployment rate. One component of this adjustment is that individuals move to lower-paying occupations. How- 1 For example, see Dougherty and Jordan (2009). 2 Empirical work finds that immigration rates are affected by changes in US wages. See Hanson and Spilimbergo (1999), Lessem (2017), and Nakajima (2014). 2

3 ever, when we look at each occupation separately, we see lower wages in most sectors during downturns. This shows that the overall wage decrease results both from lowered productivity and from shifts to lower-paying sectors. If this wage flexibility is due to undocumented status and short-term labor contracts, we should see a smaller effect in the legal immigrant population. We run the same analysis with legal immigrants in the MMP and Mexicans surveyed in the CPS, and see smaller effects. The first part of the paper shows that undocumented immigrant wages adjust over the business cycle. As wages decrease, fewer people move to the US, decreasing the supply of immigrant workers. Motivated by these facts, we build a model where Mexicans migrate between the two countries, making decisions by comparing their wage options in the two countries. After seeing an aggregate productivity shock, a representative firm hires native and immigrant workers. Native wages are fixed, and immigrant wages are determined through firm demand in response to the productivity shock and immigrant labor supply. We calibrate the parameters of the model so as to replicate the patterns in migration rates and native employment levels between 1980 and We use our results to decompose the decrease in immigrant wages during a downturn into two factors: a negative aggregate productivity shock and the supply response of immigrants. In a counterfactual experiment, we shut down the migration channel and show a stronger relationship between aggregate shocks and immigrant wages. We also show that the flexible wage setting of immigrants mitigates some native employment fluctuations over the business cycle. 3 This paper can be contrasted with the empirical findings that wages do not decrease as much as initially expected in a downturn. Hall (1980) finds that average wages fluctuate less than labor s marginal revenue product or the total volume of employment over the business cycle. 4 Other papers confirm this view, finding a weak relationship between wages and output or employment. 5 More recent work, such as Solon et al. (1994) finds evidence of wage cyclicality, claiming that the past work did not account for composition effects that gave more weight to low-skill workers in expansions than in recessions. Eslby et al. (2016) also find that real wages are procyclical, although they do report that real wages for men in the Great Recession adjust slower. We compare our results on immigrants to those on natives, testing if there are different effects in these populations. 3 We calibrate a fraction of natives whose employment is impacted by undocumented immigrants. We find that the flexible wage setting of immigrants mitigates these natives employment fluctuations. 4 The parallel discussion is on nominal wage rigidity (see, for example, Fischer (1977)). Since we focus on the response of real wages to economic fluctuations, we discuss only real wage stickiness. 5 See Abel and Bernanke (1992), Christiano and Eichenbaum (1992), Greenwald and Stiglitz (1988), Hall and Taylor (1991), and Prescott (1986). 3

4 There are numerous theoretical models to explain this trend. 6 The crucial assumption in these papers is that workers are in long-term contracts, which helps to explain this wage rigidity. This would imply that we should see greater flexibility in the undocumented immigrant population, where most jobs are short-term since contracts are not enforceable. It is beyond the scope of the paper to provide direct empirical evidence supporting that immigrants wages are more frequently re-negotiated than those of natives; instead, we focus on documenting the flexibility of immigrant wages and offer this as a potential explanation. Existing literature supports this view that undocumented immigrants labor contracts tend to be less formal. The undocumented immigrants are employed "without a legal contract that defines the terms and conditions for their jobs" and "typically contracted on an at-will basis" (Hanson, 2007). Some empirical studies support this view that newly negotiated contracts will more easily adjust to the state of the economy. Oreopoulos et al. (2012) find significant costs to graduating in a recession, meaning that workers who are just starting their career will earn lower wages during downturns. This suggests that undocumented immigrant wages would be affected by economic conditions, since wages are constantly negotiated due to the absence of long-run contracts. The paper is organized as follows. Section 2 explains the data, and the empirical analysis is shown in Section 3. The equilibrium analysis is explained in Section 4. Section 5 concludes the paper. 2. Data We use data from the MMP to estimate the relationship between immigrant wages and US economic conditions. The MMP is a repeated cross-sectional survey that started in 1987 and is still ongoing. Most respondents were surveyed in Mexico, and asked about prior moves to the US. 7 Detailed information is collected about each person s first and last move to the US, including these people s wages and legal status, enabling us to study how wages change over the business cycle. 8 The MMP also gathers a retrospective migration 6 Implicit contract theory (Baily (1974), Gordon (1974), Azariadis (1975)) explains this through a model where risk-neutral firms insure risk-averse workers. Beaudry and DiNardo (1991) find that a worker s current wage is more sensitive to the worst economic conditions since the start of a job than the contemporaneous unemployment rate. Their findings supports implicit contract theory. Search theoretic models can also be used to mimic the sluggish response of real wages in contrast to high fluctuations in unemployment (Pissarides (1985)). 7 There is also limited sampling in the US, but these sample sizes are small. 8 Unfortunately, there is only information on one job for each trip. When the duration of a trip spans multiple years, we do not know which job on the trip the data refer to. 4

5 history from household heads and spouses, enabling us to create a panel dataset with each person s location at each point in time. This will allow us to examine changes in immigrant flows over the business cycle. The panel dataset also reports each person s occupation at each point in time. We use demographic information such as age and years of education. Wages are converted to 2012 US dollars using the CPI index from the Bureau of Labor Statistics. Since workers only report one wage for the first and last trip, we use the average inflation rate over the duration of each trip. Four main data restrictions are made. First, we focus on men, since female labor force participation rates are low, making their migration decisions less influenced by the state of the economy. Second, we restrict the sample to migrants who claimed their first and last trip to be on undocumented status. Since our goal is to contrast the reaction of wages of undocumented immigrants and natives, we obtain better estimates by eliminating legal immigrants including temporary visa holders who are more likely to be working under similar conditions as natives. Third, we focus on immigrants who first visited the US after the Bracero program ( ). During this period, the US government aggressively encouraged temporary laborers to fill the shortage in the agricultural labor force after World War II. Legal workers under the program were protected, through, for example, the minimum wage. We omit these observations from our sample because of the different selection into undocumented migration in this period. In addition, since we are interested in how wages correlate with the state of the economy, we need to pinpoint each wage with a given point in time. However, the MMP reports only one wage for each trip, which means that for people with long durations of stay in the US, we do not know what point in time the wage is referring to. Therefore, we restrict the sample to those who stayed for less than 15 months. This means that the sample average of the unemployment rate over the visit duration is calculated using at most two years. Since the duration of stay correlates with wage, eliminating workers who stayed longer potentially leads to some biased results. 9 Also, this restriction results in the loss of 39% of the data. In Section 3.3, we perform robustness checks using the full sample, to make sure that this data restriction is not affecting our findings. The MMP data allow us to study how undocumented wages change over the business cycle. In particular, it is a very unique dataset because it provides information on undocumented immigrant wages, which is not possible to precisely pin down in other surveys. 10 However, there are some weaknesses of the data. In each round of the sur- 9 The direction is ambiguous. A higher wage gives an incentive to stay longer and earn more, but also allows one to accumulate savings faster and return to Mexico. 10 The Los Angeles Family and Neighborhood Survey gives information on wages and legal status, but 5

6 vey, households are randomly selected from a given set of communities to be included in the survey. The communities chosen, especially in the early years of the survey, were mostly from areas where migration to the US was prevalent. Over time, the survey has expanded into areas with lower migration rates. Nonetheless, the communities are not selected randomly. Since there are booms and recessions repeatedly throughout the years we use, we believe that this non-randomness of sampled communities is not driving our results. Furthermore, most of the sampling is done in Mexico and covers past migration histories. Because of this data collection method, households who have entirely moved to the US or households from communities with low migration rates are not included in the sample. Thus, our results only capture the characteristics of temporary migrants from communities with high migration rates. 2.1 Summary statistics Table 1 presents summary statistics on the sample of undocumented immigrants that is used in the paper. The first column looks at the full sample of men used in this paper. First, looking at demographics, we see that the sample is dominated by individuals with relatively low educational attainment, as 90% of the sample has fewer than 12 years of education. Next, we look at some summary statistics on migration behavior. The mean age of a person s first trip to the US is around 26. The average migrant has made approximately 2.6 trips to the US, and around 44% of migrants moved to the US just once. On average, each trip to the US lasts for around 40 months. Looking at wages, we find that average hourly wages are around $ Immigrants work in mainly agricultural, manufacturing, and service sectors. The second column restricts the sample to those we use in the main specification, which is people with migration durations less than 15 months. This results in a loss of a large share of the data, and we see much shorter durations based on this sample selection. The third and fourth columns look at only household heads, for whom we have some extra information, most importantly monthly wages. 2.2 Measuring recessions To study how immigrant wages change over the business cycle, we need a measure of the variation in economic conditions. To do this, we use the US unemployment rate at each point in time. Figure 1 shows variations in the unemployment rate over time for the total the samples are limited to poor neighborhoods in the Los Angeles area. 11 For the first trip to the US, wages can be reported at the hourly, weekly, or monthly level. We convert all wages to hourly wages, assuming each worker works 8 hours per day, 5 days a week, weeks per month, and every month of the year. For the last trip to the US, wages are reported at the hourly level. 6

7 population and Mexican immigrants, using data on people aged 16 and over from the CPS. Since both unemployment rates track each other closely, we use the unemployment rate for all individuals to measure the state of the economy. In the estimation, we use the unemployment rate in the state where a person is living to allow for variations across locations. Because of potential selection into low unemployment states, we repeat our analysis using national unemployment rates as a robustness check in Section Empirical evidence In this section, we study how undocumented immigrant wages change over the business cycle. We first do this using OLS, and find that wages decrease as the unemployment rate increases. The MMP data provide wages for a person s first and last trip to the US, giving us two data points for people who have moved more than once. We use this to analyze how an individual s wages change with the business cycle. 12 The decrease in immigrant wages could be due to lowered productivity or to shifts into lower-paying occupations during a downturn. We find evidence that both mechanisms are important. Overall, we see that the wages of undocumented immigrants adjust over the business cycle. We argue that a potential cause of this flexibility is the short-term nature of labor contracts in this population. If this is true, we should see less flexibility in the wages of documented immigrants from Mexico. In the last part of this section, we repeat this exercise using legal immigrants, using both MMP and CPS data. We see a larger effect of recessions in the undocumented population. 3.1 Wage levels We use OLS to estimate the effect of economic conditions on undocumented immigrant wages. To do this, we test how wages vary with the unemployment rate, while controlling for relevant demographic characteristics. In order to correctly evaluate the variations in wages over the business cycle, we need to control for any trends that are not caused by economic conditions or the distribution of demographic characteristics. The latter component is important because the MMP is a retrospective survey, and hence, the distribution of demographic factors changes over time. For example, the average age in the sample increases monotonically from 1965 to Thus, it is likely that the distribution of unobservable factors also changes over time. To account for this, we use an HP-filtered time trend. See Appendix A for details on how this was constructed. 12 This is done to attempt to control for selection. However, this is a biased sample, since we only have two observations for people who chose to move to the US more than once. 7

8 We run the following wage regression using OLS: log(w it ) τ t = α 0 + α 1 X it + α 2 u t + α 3 G t + ɛ it, (1) where w it is wages, τ t is an HP-filtered time trend, and ε it is an independent and identically distributed (i.i.d.) error. The vector X it contains individual characteristics, such as age, education, and English skills. The English skills variable is only available for household heads. The term u t is the state unemployment rate at time t, which is the average unemployment rate during the time an immigrant was in the US. We also include controls for government policies G t, which include a dummy variable for years after 1986, when the Immigration Reform and Control Act (IRCA) was passed. This law legalized most undocumented immigrants living in the US at the time and also led to increased border enforcement in future periods, and therefore could have caused changes in immigration decisions. In some specifications, we also include US border enforcement, to make sure we are capturing effects due to the state of the economy and not government policies, which are correlated according to some study. 13 Table 2 shows the results. In column (1), we use the full sample and find that a one percentage point increase in the unemployment rate lowers hourly wages by almost 2%. One concern about these results is recall bias, since the MMP surveys are conducted after migrants return to Mexico. In column (2) we only include people whose migration was within five years of the survey, to reduce recall bias, and we again see similar effects. Another concern is that changes in the state of the economy are correlated with US border enforcement efforts. In column (3), we add a control for US border enforcement, and this does not change the results. Column (4) shows the results using monthly instead of hourly wages, which are only reported for the household head s most recent trip to the US. This allows us to account for wage effects as well as unemployment effects, in that during a recession people may also find fewer hours of work. We can also control for English skills in this specification, since this variable is only available for household heads. In this case, the coefficient on the unemployment rate is still negative and statistically significant. 14 This sample is substantially different and smaller than the sample in column (1), since we only have this information for the household head s last trip to the US. To check if the household head sample is different than the full sample, in column (5) we keep the same sample as in column (4), but look at hourly wages. In this case, we see similar effects as in column (1). 13 Hanson and Spilimbergo (2001) find that border enforcement tends to relax during economic booms. 14 We find no statistically significant changes in the hours of work over the business cycle. We find similar results when we restrict the sample to those who worked for no less than 30 hours per week. 8

9 3.2 Wage growth In the previous section, we found that hourly wages decrease as the unemployment rate increases. However, as wages decrease, people become less likely to migrate, changing the composition of the migrant population. If an unobservable factor (i.e., skill) affects wage outcomes in the US, it will also affect migration decisions. As wages in the US decrease, the composition of the migrant population would change. In this setting, looking at just the average wages of those who chose to move will give biased results. In this section, we use data on repeat migrants to estimate the changes in wages during a recession while controlling for selection. We use data from immigrants who made multiple trips to analyze the relationship between wage growth and economic conditions. Because we are looking at the same individual over time, individual fixed effects cancel out and we can address this type of selection. Consider a modification of equation (1) that includes an individual fixed effect: log(w it ) τ t = α 0 + α 1 X it + α 2 u t + α 3 G t + µ i + ɛ it, (2) where µ i is an individual fixed effect that will be differenced out when we look at wage growth. Consider the changes between the first and last trip, denoted by F and L, respectively: log(w i ) τ i = α 0 + α 1 X i + α 2 u i + α 3 G i + ɛ i, (3) where log(w i ) = log(w Fi ) log(w Li ) is the difference in hourly wages between the first and last trip. 15 As in the analysis for the wage levels, we control for the state of the economy using the unemployment rate u in the state where a person is living. Hence, u i = u Fi u Li. (4) For the other explanatory variables (X i ), we use the change in age between the first and the last trip, the year of the first US migration, and the total number of trips. Table 3 shows the results. We can only look at hourly wages in this case because monthly wages are only reported for a household head s most recent trip to the US. In column (1), we see a negative and statistically significant coefficient on the unemployment 15 When we estimate this regression, we move τ i to the right hand side of the equation. This is because there are two time trends in this regression, and if we leave it on the left hand side, we lose a lot of precision due to the inclusion of two estimated terms in the dependent variable. In this regression, τ i becomes an independent variable. To keep our methodology consistent, we do this each time we run a wage growth regression in later parts of the paper. 9

10 rate, indicating that people earn lower wages in a recession. To interpret this, consider two workers with the same characteristics. Suppose worker A did not experience any economic change (i.e., u = 0) and worker B experienced a unit increase in the unemployment rate between the first and last trip (i.e., u = 1). Then worker B s wage on the last trip will be 2.9% lower than worker A s. In column (2), we add in some additional controls and do not see a substantial change in the results. In column (3), we attempt to address recall bias by using only people who had moved within five years of the survey. This substantially reduces the sample size because, for this analysis, we need two observations for each person, and there are not many respondents with both their first and last US migration with five years of the survey. In this case, the coefficient is still negative and statistically significant. In column (4), we add in controls for US border enforcement, and this does not change the baseline results. 3.3 Robustness checks In Sections 3.1 and 3.2, we find that increases in the unemployment rate lowered the wages for undocumented immigrants. However, our sample only included respondents who stayed in the US for less than 15 months. This is a strong restriction that results in a loss of almost 40% of the data. In addition, this sample is endogenously selected, and the selection may be correlated with the US unemployment rate. For example, suppose migrants who are successful in the US labor market are more likely to stay for longer durations, and US business cycles are positively correlated with those in Mexico. Then, it could be that during recession in the US, even unsuccessful migrants choose to stay longer in the US because their wages in Mexico are also going down. If this is the case, then the relationship between US unemployment and US wages will be biased upward. We control for this selection using a Heckman selection model. We use rainfall in the origin Mexican state as an instrument, following Munshi (2003) who uses rainfall in the origin community in Mexico as an instrument for the size of network at the destination. This is correlated with the length of stay in the US, but uncorrelated with US wages and the US unemployment rate. These results are shown in Table 4. As in the previous section, even after we control for selection, we see that increases in the unemployment rate lower wages. As another robustness check, we relax this assumption and include all respondents. Since only one wage is reported for each trip, it is unclear how to calculate the unemployment rate, since averaging the unemployment rate over many years can cause us to miss the effect of a downturn. We use the last year the person was in the US as the unemployment rate for each observation. We chose this instead of the first year of each trip because 10

11 certain time-varying regressors have a stronger relationship with wages when they were measured at the end of the trip instead of the start of the trip. We repeat the analysis for wage levels and growth, as in Sections 3.1 and 3.2, and the results are in Appendix B.1. In Table B.1.1, we look at the wage-level regressions. The sign of the coefficient on the unemployment rate stays the same, although it decreases in magnitude. When we restrict the sample to household heads, there is no statistically significant effect of the unemployment rate on wages. Table B.1.2 shows the wage growth regression. In all specifications, we see that an increase in the unemployment rate lowers wages. The baseline results use the unemployment rate in the state where a person is living. This leads to endogeneity concerns since people can select into low unemployment states. In Appendix B.2, we use national unemployment rates as an additional robustness check. 16 Table B.2.1 shows the wage-level regressions, and the results are very similar to the baseline specification. Table B.2.2 shows the results for wage growth, and we see a negative effect of the unemployment rate on wages in all specifications. 3.4 Occupation changes The results in the previous sections show that as the unemployment rate increases, immigrants earn lower wages. We do not control for occupations, suspecting that occupations could be a choice variable that varies with the business cycle. In this section, we study what happens to occupations over the business cycle. We estimate the probability that a person works in a given sector (agriculture, skilled manufacturing, unskilled manufacturing, transportation, services, and sales) using a multinomial logit regression. We control for demographic factors, a time trend, primary occupation in Mexico, and occupation held in the previous year. The MMP reports a worker s occupation for each year of US stay, so for this analysis, we also include immigrants who stayed for more than 15 months. 17 The results, reported as marginal effects in Table 5, demonstrate that, as the unemployment rate increases, people are more likely to work in agriculture, unskilled manufacturing, and sales, and less likely to work in skilled manufacturing. This suggests that people shift to different occupations over the business cycle. In Table 6, we repeat the OLS wage regressions in Section 3.1 using controls for occu- 16 Data on state unemployment rates are from the Local Area Unemployment Statistics from the Bureau of Labor Statistics. These are seasonally adjusted values. 17 In the wage analysis, we had to drop these people, since wages are only reported for the first and last trip, so we do not know the year of each wage for people with long durations of stay in the US. In the occupation analysis, we can include these people since we have occupations in each year. We find a similar result when we restrict to those who stayed in the US for less than 15 months. 11

12 pations. The first column uses all men, and the second column uses just male household heads (allowing us to control for English skills). The third column restricts to wages within the past five years to limit recall bias, and column (4) uses all men and adds controls for border enforcement. In all specifications, we see that both skilled and unskilled manufacturing jobs pay more than jobs in agriculture. Since more people work in agriculture in downturns and agriculture pays lower wages, these occupational changes lead to lower average wages when the unemployment rate increases. This is one component of reduced wages in a recession. In addition, we see that even with these occupation controls, an increase in the unemployment rate lowers wages. The previous wage regressions allowed for the effects of economic conditions to be constant across sectors. In Table 7, we run the regressions separately for agriculture, non-agriculture, skilled manufacturing, unskilled manufacturing, and services, which are the occupations that the most migrants work in. In all of these sectors, increases in the unemployment rate lower wages, although the effect is smallest and insignificant in services and agriculture. One possible reason for our findings in the agricultural sector is changes in the composition of immigrants across sectors. If more productive workers usually work in skilled manufacturing but shift to agriculture during recessions, then the average productivity of workers in agriculture increases during recessions. We do not find a statistically significant effect in other sectors that are not reported Comparison: legal immigrants In the previous section, we saw that increases in the unemployment rate lowered wages for undocumented immigrants in the US. These workers typically only obtain short-term jobs, meaning they constantly are renegotiating wages, which can then easily respond to the state of the economy. This implies we should see smaller effects for documented immigrants. We test this using data from the CPS and the MMP. The CPS does not record legal status, but likely overweights legal Mexican workers relative to undocumented immigrants. In addition, we follow the methodology used in Borjas (2016) to separate likely legal from undocumented immigrants. The MMP asks survey questions on legal status, so we can isolate the sample of documented workers. Below, we first compare wage fluctuations across Mexican-born individuals and native US workers. We then compare wage fluctuations across likely legal and undocumented immigrants by applying the method in Borjas (2016). We analyze how wages respond to the unemployment rate using data on Mexicanborn individuals (both with and without US citizenship) and native US workers in the 18 The sample sizes get much smaller when we look at sectors that fewer immigrants work in. 12

13 CPS. We study how changes in the unemployment rate affect the wages of these workers. The sample contains men aged from Table 8 shows the wage regressions for Mexican-born workers (both with and without US citizenship) and USborn workers. Column (1) shows that for Mexican-born non-us citizens, a 1% increase in the unemployment rate reduces weekly earnings by 0.8%. This is smaller than the effect that we saw in the MMP sample. In addition, we do not see a statistically significant effect of the unemployment rate on earnings for US citizens. The MMP sample has lower levels of education than Mexicans in the CPS sample, so one concern could be that these results are being driven by sample composition and not legal status. This is particularly relevant because past work such as Hoynes et al. (2012) finds that recessions hit the lower-skilled workers hardest. To account for this, in column (2) we restrict our sample to Mexicans with no more than a high school education, and the results do not change. In columns (3) and (4) we look at native US workers. For the full sample of US-born people who are white or Hispanic, we find almost no impact on weekly earnings during recessions. We only see a statistically significant effect of the unemployment rate on wages when we restrict the sample to white non-hispanics with just a high school education. This effect is smaller than in the MMP, supporting our theory. 20 Since the CPS MORG data provide wage information for two consecutive years for each individual, we next test how a person s wage changes with the unemployment rate. As before, this controls for individual fixed effects. We regress w 2 w 1 against u 2 u 1, where w t is the reported wage and u t is the unemployment rate in the t-th interview. Table 9 shows the regression results for Mexican- and US-born workers. A 1% increase in a subsequent year s unemployment rate reduces Mexicans weekly earnings by 1.8%, and there is no effect in the native population. This clearly suggests immigrant wages are more flexible than native wages. These effects are again smaller than what we found using the sample of undocumented workers in the MMP. In Tables 8 and 9, we did not know the legal status of each respondent, since that is not included in the CPS. Therefore those results are some mix of the two populations. Borjas (2016) develops a methodology using the ASEC files in the CPS to try to identify undocumented immigrants. He defines a person as a legal immigrant if one of the following conditions holds: that person was born before 1980, is a citizen, receives government benefits 21, is a veteran or is in the military, works in the government sector, resides in 19 We cannot use earlier data because information on country of birth is only available starting in We ran a regression using Mexican and native workers, combining columns (2) and (4). When we did this, we were able to reject the null hypothesis that the effect of the unemployment rate is the same in both groups. 21 Specifically, if that person receives Social Security benefits, SSI, Medicaid, Medicare, or military insurance 13

14 public housing or receives rental subsidies (or that person s spouse does), was born in Cuba, works in an occupation that requires licensing, or is married to a legal immigrant or citizen. We use this methodology to separate legal and undocumented immigrants in the ASEC data. 22 We then estimate the effect of the unemployment rate on wage levels, and the results are in Table 10. Column (1) shows the whole sample of Mexican-born individuals in the ASEC, column (2) shows just those who we identify as undocumented immigrants, and column (3) shows those who we identify as legal immigrants. We only see a negative and statistically significant effect of the unemployment rate on wages in the undocumented immigrant group. When we look at wage growth in the ASEC, we see that an increase in the unemployment rate lowers wages. Column (1) of Table 11 shows this effect for the whole sample. In column (2), which only looks at undocumented immigrants, the magnitude of the coefficient increases, whereas in column (3), which only includes legal immigrants, we see no statistically significant effect of the unemployment rate on wages. This supports our hypothesis that we are seeing a wage effect in a segment of the population more likely to work under short-term contracts. Finally, we run another comparison using data on legal immigrants from the MMP. Table 12 shows the results of the wage regression using MMP data and allowing for differential effects for legal and undocumented immigrants. We find that the wages of undocumented immigrants drop during recessions, while we see almost no impact on those of legals. These results support our findings using the CPS data when we looked at documented and undocumented immigrants separately. The empirical evidence shows that wages of undocumented immigrants drop during recessions. We find a smaller effect in samples that include legal immigrants, suggesting that the effect is due to short-term contracts. These results demonstrate changes in the equilibrium wage, which reflects both aggregate demand and the supply of immigrant workers. In the next section, we first show that migration rates respond to these lower wages in downturns, hence lowering the supply of migrants. This could potentially raise immigrant wages. We then analyze an equilibrium model to account for this mechanism. 4. Equilibrium analysis The previous sections suggest that undocumented Mexican workers experience larger wage drops during recessions than natives. Undocumented immigrants are more likely to work under short-term contracts than native workers, and in this setting wages will be 22 Most of these classifications are straightforward. Occupational licensing is done at the state level. We define an occupation as needing licensing if more than half of the states require licensing for that job. The licensing data were collected from the O*NET database. 14

15 more flexible since they must be constantly renegotiated. Since migration patterns change over the business cycle, the supply of immigrant labor in the US fluctuates. The goal of this section is to understand how these specific features impact immigrant wages and native employment fluctuations over the business cycle. 4.1 Migration response to recessions Before we present our model, we show how migration rates change over the business cycle. We run a probit regression, testing whether a person who is living in Mexico chooses to move to the US during that year. We regress an indicator for migration to the US against the US unemployment rate, the US real GDP growth rate, and the Mexican unemployment rate. 23 We control for age, education, marital status, an HP-filtered time trend, and whether or not a person is from a high migration state in Mexico. 24 Table 13 shows the results. The negative coefficient on the US unemployment rate implies that when the US is in a recession, fewer workers migrate to the US. We also did the same exercise looking at return migration rates (Table 14). Although the estimated coefficients are not statistically significant, we find a positive coefficient on the US unemployment rate, indicating that more workers return to Mexico during a time when the US is in a recession. 4.2 Model We use a static equilibrium model to highlight how migration decisions and the flexible wage setting of undocumented immigrants affect their wages and natives employment fluctuations over the business cycle. We consider a representative firm who hires native workers and immigrant workers to produce output. 25 In the model, Mexicans decide which country to reside in based on relative wages and the cost of crossing the border. 23 US GDP is in billions of chained 2005 dollars taken from Bureau of Economic Analysis. The Mexican unemployment rates are taken from the MMP for and from the OECD for These are the states with the highest migration rates, where migrant networks are strong and hence people may be more likely to migrate. These states are Aguascalientes, Durango, Guanajuato, Hidalgo, Jalisco, Michoacan, Morelos, Nayarit, San Luis Potosi, and Zacatecas. 25 In an alternative setup, we could allow the firm to hire skilled native labor and to purchase an intermediate input, which is produced by unskilled native and immigrant labor. This intermediate good setup can be explained by immigrants working in, for example, the construction sector, where they do the basic tasks and then the legal and native workers do the more complicated tasks, as in the framework in Djajic (1997). This setup is more realistic because it allows for the possibility that the US can produce without undocumented immigrants labor; however, we use a simpler model here which demonstrates the key mechanisms. 15

16 We assume that native wages are exogenous to the model and denote them as w Nt at period t. This assumes a perfectly elastic labor supply curve, and captures the empirical fact that native wages are less flexible than immigrant wages. Wages for immigrants in the US w It are determined endogenously through the supply and demand for immigrants in the US. We assume that wages in Mexico are exogenous and denote them as w Mt Firm side A firm s output in period t depends on aggregate productivity z t and the quantity of native and immigrant workers, N t and I t, respectively. We write the firm s output as z t F(N t, I t ). Given wages w Nt and w It, the firm solves the following profit maximization problem: where max N t,i t z t F(N t, I t ) w Nt N t w It I t, (5) F(N t, I t ) = ( [θn γ t + (1 θ)i γ t ]1/γ) ψ. (6) In equation (6), we use a Cobb-Douglas function with parameter ψ, where we assume decreasing returns to scale in labor so 0 < ψ < 1. The two inputs into the Cobb-Douglas function are capital (which we assume to be fixed) and labor, which is a CES aggregation of native and immigrant labor. Because the model is static, the firm only considers current-period profits when making hiring decisions. The first-order conditions are which implies w Nt = z t ψ[θn γ t + (1 θ)i γ t ] ψ γ 1 θn γ 1 t (7) w It = z t ψ[θn γ t + (1 θ)i γ t ] ψ γ 1 (1 θ)i γ 1 t, (8) N t = ( ) 1 θ w It 1 γ 1 θ w Nt I t. (9) Because immigrant wages are flexible, there is no unemployment for immigrants. Let I D t (w It, w Nt ) denote the firm s demand for immigrant workers. We assume that native labor is abundant and that the wage for natives is sufficiently high so that the number of natives hired by the firm is always given by the first-order conditions. When solving the model, we also assume that N natives are always hired at a different firm, and we calibrate the value. This reflects the reality that not all natives work at firms that hire undocumented immigrants. 26 This model setup allows us to match a situation where immigrants wages are more flexible than native wages while at the same time immigrant employment is more volatile than natives. 26 We impose this assumption to make the model more flexible. Since I and N move in one-to-one (in 16

17 4.2.2 Worker side We assume that a worker s location at the start of the period l t is exogenous to the model. He can be living in the US or Mexico, and then decides which of the two countries to live in for the next period by comparing the value of living in each location. These values have three components: the wages in each country, the cost of moving, and the payoff shocks. Wages vary between the US and Mexico, and the wage function is written as: w t (ˆl) = w Mt w It if ˆl = MEX if ˆl (10) = US. The cost of moving from l t to ˆl is denoted by c(l t, ˆl), and is defined as follows: c 1 if l t = MEX and ˆl = US c(l t, ˆl) = c 2 if l t = US and ˆl = MEX 0 otherwise. (11) Moving to a new location has a constant cost, which varies for Mexico to US migration and return migration. The cost of moving to the US is given by c 1, and the cost of returning to Mexico is given by c 2. If a worker stays in the same location, the cost is 0. We assume that utility is linear in wages with coefficient α. Denote the set of payoff shocks as η = {η US, η MEX }, and we assume these are distributed with an extreme value type I distribution. Because the model is static, individuals pick the location with the highest per-period valuation. Then the value function is given by V t (l t ) = { max α(w t (ˆl) c(l t, ˆl)) } + ηˆl. (12) ˆl {US,MEX} We use the model to calculate the probability that a person lives in the US in each period, conditional on his location at the start of the period. Following McFadden (1973) and Rust (1987), these probabilities p t (l t ) are given by p t (MEX) = exp(α(w It c 1 )) exp(α(w It c 1 )) + exp(αw Mt ) (13) log-terms) in the CES production function, the volatility of native employment must equal that of the immigrant labor in the model. However, in the data, the volatility of native employment is lower than that of the immigrants over the business cycle. Therefore, in order to accommodate these differences, we assume that there is some proportion of natives who are always hired at a different firm and calibrate this proportion. 17

18 p t (US) = exp(αw It ) exp(αw It ) + exp(α(w Mt c 2 )). (14) Equation (13) gives the probability that someone who is in Mexico moves to the US, and equation (14) gives the probability that someone who already lives in the US stays there. Assume there are I1t 0 people in Mexico at the start of period t and I0 2t people in the US at the start of period t. Then the total supply of immigrants in the US after workers make their migration decisions is I S US,t (w It) = p t (MEX)I 0 1t + p t(us)i 0 2t. (15) We can then equate labor supply and demand to get the equilibrium condition for immigrant wages w It : I D t (w It, w Nt ) = I S US,t (w It), (16) where I D t (w It, w Nt ) comes from the firm s first-order conditions in equations (7) and (8). To summarize, the variables determined in equilibrium are the immigrant wage w It, immigrant labor in the US I S US,t (w It), and native employment N t. These are solved using the firm s first order conditions and the labor market clearing condition. We end this section with an explanation of our choice of the firm s maximization problem. It is natural to think that the native-immigrant wage gap comes from a "penalty" that a firm faces when hiring undocumented immigrants. This "penalty" potentially takes many forms. For example, IRCA (1986) prohibits firms from knowingly hiring undocumented workers, creating a cost in expectation from hiring undocumented workers. Also, immigrants may be less productive compared to the natives due to lower English skills. Moreover, immigrant workers may be more likely to quit a job because return migration is relatively common and because migrants are more mobile than natives within the US. 27 However, it is impossible to separately identify lower productivity and an additional hiring cost in the MMP data. Therefore, instead of considering an additional hiring cost for each immigrant worker, we assume that all costs associated with hiring an immigrant are captured through the substitutability of immigrant and natives in the production function. 4.3 Analytical exercises In this section, we analytically show how the flexible wage setting of immigrants affects the employment fluctuations of natives over the business cycle. We drop the time subscripts in this section for ease of exposition. 27 See Cadena and Kovak (2015). 18

19 We first show that the equilibrium wage exists and is unique. Remark 1. If c 2 or I 0 2 is sufficiently small, an equilibrium with w I > 0 uniquely exists. Proof. See Appendix C. Note that in equation (8), a higher z t shifts the demand curve upward, which increases the equilibrium wage of immigrants (i.e., w It z t > 0). Thus, this simple model is able to capture a possible economic mechanism that allows for changes in immigrant wages over the business cycle. In the model, the equilibrium wage is low when aggregate productivity is low, which is consistent with the observed phenomenon. The next remark shows that the flexible wage setting of immigrants mitigates (respectively accelerates) native labor demand fluctuations over the business cycle if native and immigrant labor are complements (respectively substitutes). Remark 2. If γ is a large negative number, N z close to 1, N z > N z w I = w I. < N z w I = w I where w I is a constant. If γ is Proof. See Appendix C. This remark shows that the effect of the flexibility of immigrant wages on native outcomes is ambiguous. In the empirical section, we will calibrate the model parameters, and then be able to understand how this immigrant wage flexibility impacts natives. 4.4 Calibration procedure This section explains how the time-invariant model parameters Θ (α, γ, c 1, c 2, N), endogenous variables (natives labor N t, immigrant stock in the US I t ), and TFP z t are solved for. 28 We assume ψ = 0.67, which is the labor share of output used in the macroeconomics literature. We assume θ = 0.5. We calibrate the model using a procedure similar to indirect inference. We take the following values from the data: wages in Mexico w Mt, native wages w Nt, immigrant wages w It, and the stock of Mexicans in the US and in Mexico at the start of the period, I1t 0 and I0 2t, respectively. For a given set of parameters Θ, we solve for the Mexico to US migration rate p t (MEX) and the rate of staying in the US p t (US) using equations (13) and (14). Using equation (15), we can then solve for the supply of immigrants in the US in a period (again conditional on our parameter guess Θ). We then solve for N t using the 28 For simplicity, we assume that the capital is constant over time. 19